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Which, whose, and how much knowledge do LLMs represent? I'm excited to share our preprint answering these questions: "Epistemic Diversity and Knowledge Collapse in Large Language Models" 📄Paper: arxiv.org/pdf/2510.04226 💻Code: github.com/dwright37/ll... 1/10

🤔 A lot of people are using LLMs. However, their outputs are not very diverse. What does this mean for the future of knowledge? Many speculate that overreliance on LLMs will lead to "knowledge collapse", where the diversity of human knowledge is narrowed by a reliance on homogenous LLMs. 2/10

📈 Knowledge in LLMs across 3 of 4 model families has *expanded* since 2023 ✅ ; however, their absolute diversity is quite low compared to a very modest traditional search baseline 👎 4/10

🏗️ Model size has an unintuitive negative impact on diversity; smaller models tend to be more diverse 🔎 RAG has a positive impact on diversity, indicating its usefulness in making LLM outputs more diverse. However, the gains from RAG are not equal across topics about different countries 5/10

🌍 There are gaps in country specific knowledge. When matching claims to English and local language Wikipedia, no local language is statistically significantly more represented than English, and English language knowledge is statistically significantly more represented for 5 of 8 countries 6/10